Driving behavior feedback interface

ABSTRACT

One or more embodiments of the present application may provide a system and method for monitoring driver inputs and vehicle parameters, assessing a driver&#39;s acceleration behavior, and providing short-term and/or long-term feedback to the driver relating to the driver&#39;s acceleration behavior. The acceleration behavior feedback can be used to coach future driving acceleration behavior that may translate into better long-term driving habits, which in turn may lead to improvements in fuel economy or vehicle range. Moreover, the acceleration behavior feedback can be adapted to a driver based upon how responsive the driver is to the feedback.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional Application No.61/581,940. filed Dec. 30, 2011, the disclosure of which is incorporatedin its entirety by reference herein.

TECHNICAL FIELD

One or more embodiments of the present application relate to a systemand method for conveying feedback to a driver on the driver'sacceleration behavior via a user interface.

BACKGROUND

Vehicles include a number of interfaces, such as gauges, indicators, andvarious displays to convey information to the user regarding thevehicle's operation and its surroundings. With the advent of newtechnologies, including technologies found in conventional vehicles aswell as in hybrid electric vehicles (HEVs), plug-in hybrid electricvehicle (PHEVs) and battery electric vehicles (BEVs), these interfaceshave become more sophisticated. For example, many HEVs incorporategauges that attempt to provide the driver with information on thevarious hybrid driving states. Some gauges will indicate to the driverwhen the vehicle is being propelled by the engine alone, the motoralone, or a combination of the two. Similarly, a display may indicatewhen the motor is operating as a generator, and is recharging an energystorage device, such as a battery. Regardless of the vehicle type, fueleconomy or range of a vehicle still remains an important metric to mostvehicle drivers.

In real world driving conditions, driver behavior remains the primaryfactor affecting fuel economy or range of a vehicle. It is known thatsome drivers may not be able to achieve desired fuel economy or range,in part because of driving habits. Although it is clear that drivingbehavior affects the fuel economy or range of a vehicle, it is oftenunclear how one should drive by taking powertrain and otherenvironmental factors into account in order to improve fuel economy orrange. In many cases, drivers are willing to modify their behavior, butare unable to translate recommended techniques into real changes intheir driving habits.

SUMMARY

According to one or more embodiments of the present application, adisplay control system and method for coaching driving accelerationbehavior is provided. The control system may include a controller and aninterface in communication with the controller. The controller may beconfigured to receive input indicative of at least vehicle accelerationand powertrain output power. The controller may be further configured tooutput at least one acceleration score based upon the input. Theinterface may be configured to display an acceleration feedbackindicator indicative of the at least one acceleration score.

The interface may include an acceleration feedback gauge for displayingthe acceleration feedback indicator. The interface may be configured toadjust the acceleration feedback indicator within the accelerationfeedback gauge based on the at least one acceleration score. The atleast one acceleration score indicated by the acceleration feedbackindicator may include one of a long-term acceleration score and aninstantaneous acceleration score. Moreover, the interface may be furtherconfigured to adjust a color of at least a portion of the accelerationfeedback gauge based on the other of the long-term acceleration scoreand the instantaneous acceleration score.

According to one or more embodiments, the input may be furtherindicative of an accelerator pedal position change. The controller maycalculate the instantaneous acceleration score based upon the vehicleacceleration, the powertrain output power and the accelerator pedalposition change. In this regard, the controller may normalize one ormore of the vehicle acceleration, the powertrain output power and theaccelerator pedal position change based upon vehicle speed prior tocalculating the instantaneous acceleration score. Moreover, thecontroller may calculate an adapted acceleration value prior tocalculating the instantaneous acceleration score. The adaptedacceleration value may be based on the vehicle acceleration and thelong-term acceleration score. For instance, the adapted accelerationvalue may be calculated by multiplying a normalized acceleration valueby the long-term acceleration score.

According to one or more embodiments, the instantaneous accelerationscore may be calculated using a fuzzy logic algorithm. Furthermore, thelong-term acceleration score may be based at least in part upon theinstantaneous acceleration score, a previous long-term accelerationscore, and a forgetting factor for weighting the instantaneousacceleration score and the previous long-term acceleration score. Avalue associated with the forgetting factor may be based on a comparisonof the instantaneous acceleration score to a function of the long-termacceleration score.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level, functional diagram of a vehicle control systemfor coaching driving behavior in accordance with one or more embodimentsof the present application;

FIG. 2 is an exemplary, functional block diagram of the control systemin greater detail;

FIG. 3 is a simplified, schematic block diagram of the controller andrelated algorithms generally described in FIG. 2 for use in coachingdriving acceleration behavior;

FIG. 4 is a functional block diagram illustrating a fuzzy logic controlalgorithm in accordance with one or more embodiments of the presentapplication;

FIGS. 5 a-c depict exemplary input membership functions for use ingenerating input membership values based on a set of fuzzy inputvariables in accordance with one or more embodiments of the presentapplication;

FIG. 6 is a table illustrating an exemplary set of fuzzy rules inaccordance with one or more embodiments of the present application;

FIG. 7 depicts a simplified, exemplary output membership function fordetermining output membership values in accordance with one or moreembodiments of the present application;

FIG. 8 is an exemplary flow diagram an implementation of adeffuzzification process using a real-world example in accordance withone or more embodiments of the present application;

FIG. 9 is a simplified, exemplary flow chart depicting a method forconveying driving acceleration behavior feedback in accordance with oneor more embodiments of the present application; and

FIG. 10 is a simplified, exemplary flowchart depicting a method forcalculating an instantaneous acceleration score in accordance with oneor more embodiments of the present application.

DETAILED DESCRIPTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousand alternative forms. The figures are not necessarily to scale; somefeatures may be exaggerated or minimized to show details of particularcomponents. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for teaching one skilled in the art to variouslyemploy the present invention.

One of the main driver factors than can affect the fuel efficiency orrange of a vehicle is the driving acceleration behavior as dictated bythe driver's accelerator pedal maneuver. Many drivers are oftenuncertain how they should drive in order to improve fuel economy orrange by taking powertrain and other environmental factors into account.Feedback to drivers on their driving acceleration behavior can impact orimprove their future actions to increase fuel economy or range withminimal, if any, effect on the drivability of the vehicle. Real-timedriving acceleration behavior feedback can translate into betterlong-term driving habits.

One or more embodiments of the present application may provide a systemand method for monitoring driver inputs and vehicle parameters,assessing a driver's acceleration behavior, and providing feedback tothe driver relating to the acceleration behavior. The drivingacceleration behavior feedback can be used to coach the driver's futuredriving acceleration behavior. The driving acceleration behaviorcoaching may ultimately lead to improvements in the vehicle's powerefficiency when the current driving acceleration behavior negativelyaffects or reduces the power efficiency of the vehicle.

The system can provide relatively short-term feedback or advice relatingto a driver's driving acceleration behavior. Moreover, the system maymonitor the driver's acceptance or rejection of the short-term feedbackin order to learn the driver's long-term intentions for using thefeedback to modify his or her driving acceleration behavior. Further,the system may provide a long-term score relating to the driver'sdriving acceleration behavior that may be based, at least in part, uponthe driver's acceptance or rejection of the driving accelerationbehavior feedback. In this manner, the system can adapt to the driver'slong-term intentions regarding use of the acceleration behavior coachingto modify driving habits and can provide corresponding feedback that maytend to improve the driver's acceleration behavior gradually over time.According to one or more embodiments of the present application, thelong-term score relating to the driver's acceleration behavior may beused to modify the system's vehicle acceleration input, which may beused in generating the short-term feedback when the accelerator pedalposition, the vehicle speed, and the acceleration are each above certainthresholds.

Referring now to the drawings, FIG. 1 depicts a high-level, functionaldiagram of a control system 20 for a vehicle (not shown) for coachingdriving behavior in accordance with one or more embodiments of thepresent application. The control system 20 may include a controller 22and a user interface 24 that are in communication with each other.Although it is shown as a single controller, the controller 22 mayinclude multiple controllers that may be used to control multiplevehicle systems. For example, the controller 22 may be a vehicle systemcontroller/powertrain control module (VSC/PCM). In this regard, the PCMportion of the VSC/PCM may be software embedded within the VSC/PCM, orit can be a separate hardware device. The controller 22 generallyincludes any number of microprocessors, ASICs, ICs, memory (e.g., FLASH,ROM, RAM, EPROM and/or EEPROM) and software code to co-act with oneanother to perform a series of operations. The controller 22 maycommunicate with other controllers (e.g., a battery energy controlmodule, transmission control module, etc.) and the user interface 24over a hardline vehicle connection, such as a BUS 25, using a common busprotocol (e.g., CAN), or may communicate wirelessly with other vehicledevices using a wireless transceiver (not shown).

The controller 22 may receive input signals 26 and may generate one ormore instantaneous and/or long-term driving behavior feedback signals 28in response to the input signals 26. The controller 22 may transmit thisinformation to the user interface 24, which in turn conveys theinformation to the driver. The driver may then use the driving behaviorfeedback to improve driving habits, such as those relating toacceleration, deceleration and cruising.

The user interface 24 may include at least one display 30 and associatedcircuitry, including hardware and/or software, necessary to communicatewith the controller 22 and operate the display. The display 30 may begenerally used to convey relevant vehicle content to a driver of thevehicle including, for example, driving behavior information or otherinformation relating to the operation of the vehicle.

The display 30 may be disposed within a dashboard (not shown) of thevehicle, such as in an instrument panel or center console area.Moreover, the display 30 may be part of another user interface system,such as a navigation system, or may be part of a dedicated informationdisplay system. The display 30 may be a liquid crystal display (LCD), aplasma display, an organic light emitting display (OLED), or any othersuitable display. The display 30 may include a touch screen forreceiving driver input associated with selected areas of the display.The user interface 24 or display 30 may also include one or more buttons(not shown), including hard keys or soft keys, for effectuating driverinput.

The driving behavior feedback signals 28 generated by the controller 22may correspond to a score or other relative metric that may be used toevaluate aspects of a driver's driving behavior, such as accelerationbehavior, deceleration (braking) behavior and cruising speed behavior.According to one or more embodiments, the driving behavior feedbacksignals 28 may include one or more of the following driving behaviorscores: an instantaneous acceleration score (S_(a)), a long-termacceleration score (L_(a)), an instantaneous deceleration score (S_(d)),a long-term deceleration score (L_(d)), an instantaneous cruising speedscore (S_(c)), and a long-term cruising speed score (L_(c)).

The display 30 may include one or more driving behavior feedback gauges32 for conveying the various driving behavior feedback scores. Inparticular, the display 30 may include an acceleration feedback gauge 32a associated with the instantaneous acceleration score (S_(a)) and/orthe long-term acceleration score (L_(a)). The display 30 may furtherinclude a deceleration feedback gauge 32 b associated with theinstantaneous deceleration score (S_(d)) and/or the long-termdeceleration score (L_(d)). Furthermore, the display 30 may include acruising speed feedback gauge 32 c associated with the instantaneouscruising speed score (S_(c)) and/or the long-term cruising speed score(L_(c)). As shown in FIG. 1, each driving behavior feedback gauge 32 maybe a bar gauge including at least one feedback indicator correspondingto at least one of the driving behavior feedback signals 28. Forinstance, the acceleration feedback gauge 32 a may include anacceleration feedback indicator 34 corresponding to at least one of theinstantaneous acceleration score (S_(a)) and the long-term accelerationscore (L_(a)). Similarly, the deceleration feedback gauge 32 b mayinclude a deceleration feedback indicator 36 corresponding to at leastone of the instantaneous deceleration score (S_(d)) and the long-termdeceleration score (L_(d)). The cruising speed feedback gauge 32 c mayinclude a cruising speed feedback indicator 38 corresponding to at leastone of the instantaneous cruising speed score (S_(c)) and the long-termcruising speed score (L_(c)). Each feedback indicator may define acorresponding bar segment illuminated or otherwise displayed by thedisplay 30. Accordingly, the driving behavior score corresponding toeach feedback indicator may define the length of its associated barsegment. For example, the acceleration feedback indicator 34 may definean acceleration bar segment 40 on the acceleration feedback gauge 32 a,the deceleration feedback indicator 36 may define a deceleration barsegment 42 on the deceleration feedback gauge 32 b, and the cruisingspeed feedback indicator 38 may define a cruising speed bar segment 44on the cruising speed feedback gauge 32 c. Although each drivingbehavior feedback gauge 32 may be implemented using a bar gauge orsimilar graphic, various alternate types of gauges and/or indicators mayalso be employed to convey the driving behavior scores. Somenon-limiting examples may include numerical indicators, needle gauges,and the like.

One or more embodiments of the present application may be implemented inall types of vehicles, including vehicles having different powertrainconfigurations. For example, one or more embodiments may be implementedin hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles(PHEVs), battery electric vehicles (BEVs), or conventional vehicles,such as those powered solely by an internal combustion engine. HEVs mayrefer to vehicles powered by an engine and/or one or more electricmotors. BEVs may refer to all-electric vehicles propelled by one or moreelectric motors without assistance from an internal combustion engine.PHEVs may refer to hybrid electric vehicles primarily powered by one ormore electric motors. PHEVs and BEVs may be connected to an externalpower supply for charging a vehicle battery that supplies electricalpower to the motors.

In order to provide one or more of the driving behavior feedback signals28 referenced above, one or more of the input signals 26 received by thecontroller 22 may be generally indicative of vehicle speed (V_(spd)),actual vehicle acceleration (A_(actual)), and/or actual vehicledeceleration (D_(actual)). In addition, one or more of the input signals26 may be generally indicative of total powertrain output power(P_(total)), accelerator pedal position change (ΔAcc_Ped) and/or brakingpercentage (Pct_Brk). The input signals 26 received by the controller 22may be used in one or more algorithms contained within, or otherwiseexecuted by, the controller 22 for determining input values such asvehicle acceleration (A_(actual)), deceleration (D_(actual)), totalpowertrain output power (P_(total)), accelerator pedal position change(ΔAcc_Ped) and/or braking percentage (Pct_Brk). Although generallydescribed as inputs received directly by the controller 22, one or moreof the input signals 26 may be merely indicative of inputs generallyused in controller algorithms for generating the driving behaviorfeedback. To this end, exemplary input signals may include anaccelerator pedal position signal (APPS), a brake switch signal (Brk_SW)and/or brake pedal flag signal (Brk_Ped_Flg), friction braking torque(T_(friction)), regenerative braking torque (T_(regen)), high-voltage(HV) battery power (P_(batt)), fuel flow rate (Fuel_Flow), vehicle speed(V_(spd)) or output shaft speed (ω_(oss)), vehicle mode (Veh_Mode), andthe like.

The inputs may be received directly as input signals from individualsystems or sensors (not shown), or indirectly as input data over the CANbus 25. The input signals 26 received by the controller 22 may bedependent on the powertrain technology employed in a particular vehicle.For instance, in conventional vehicle applications, the input signalsrelating to the HV battery power (P_(batt)) or regenerative brakingtorque (T_(regen)), for example, may not be present or applicable ingenerating the driving behavior feedback signals 28. Similarly, in BEVapplications, an input signal corresponding to the fuel flow rate(Fuel_Flow) would not be applicable.

The controller 22 may determine the actual vehicle acceleration(A_(actual)) and deceleration (D_(actual)) from the actual vehicle speed(V_(spd)) or output shaft speed (ω_(oss)). The controller 22 maydetermine the total powertrain output power (P_(total)) a number of waysdepending upon the powertrain configuration. For instance, the totalpowertrain output power (P_(total)) in HEV and PHEV applications may bethe sum of the battery power (P_(batt)) from a high voltage battery andfuel power (P_(fuel)) as set forth below:

P _(total) =P _(batt) +P _(fuel)   Eq. 1

The fuel power (P_(fuel)) may be calculated using the value from thefuel flow rate (Fuel_Flow) and a fuel density (Fuel_Density) accordingto Eq. 2 set forth below:

P _(fuel)=Fuel_Flow×Fuel_Density   Eq. 2

In BEV applications, however, the total powertrain output power(P_(total)) may be based solely on the battery power (P_(batt)):

P_(total)=P_(batt)   Eq. 3

In conventional powertrain applications, the total powertrain outputpower (P_(total)), may be based solely on the fuel power (P_(fuel)):

P_(total)=P_(fuel)   Eq. 4

The controller 22 may determine the accelerator pedal position change(ΔAcc_Ped) from the accelerator pedal position signal (APPS), which mayrepresent a driver request for wheel torque/power. Therefore, theaccelerator pedal position change (ΔAcc_Ped) may be indicative of thedriver's accelerator pedal response.

FIG. 2 is an exemplary, functional block diagram of the control system20 in greater detail. As seen therein, the controller 22 may include aplurality of interrelated algorithms, represented as distinct blocks,for generating the driving behavior feedback signals 28. Althoughseveral of the interrelated algorithms have been divided upschematically in FIG. 2 for illustrative purposes, they me be combinedinto one larger algorithm for generating the driving behavior feedbacksignals 28 transmitted to the user interface 24. As shown in FIG. 2, theinput signals 26 described with respect to FIG. 1 may be generallyreceived at an input process and normalization block 46. Within theinput process and normalization block 46, one or more of the inputsignals 26 may be processed to obtain the values for vehicleacceleration (A_(actual)), deceleration (D_(actual)), total powertrainoutput power (P_(total)), accelerator pedal position change (ΔAcc_Ped),braking percentage (Pct_Brk) or the like, as described above. Moreover,the vehicle acceleration (A_(actual)) and deceleration (D_(actual)) maybe modified as a function of vehicle speed (V_(spd)) to obtain anormalized acceleration value (A_(norm)) and a normalized decelerationvalue (D_(norm)), respectively.

The total powertrain output power (P_(total)) may also be modified as afunction of vehicle speed (V_(spd)) to generate a normalized totalpowertrain output power value (P_(norm)). Similarly, the acceleratorpedal position change (ΔAcc_Ped) may also be modified as a function ofvehicle speed (V_(spd)) to obtain a normalized accelerator pedalposition change value (ΔAcc_Ped_(norm)). In some instances, the vehiclespeed (V_(spd)) itself may be normalized to obtain a normalized vehiclespeed (V_(norm)).

Like the total powertrain output power, the controller 22 may determinebraking percentage (Pct_Brk) differently based on the powertrainconfiguration. For HEVs, PHEVs, and BEVs, the braking percentage(Pct_Brk) may be based upon a ratio of regenerative braking torque(T_(regen)) to the sum of friction braking torque (T_(friction)) andregenerative braking torque (T_(regen)). For instance, the brakingpercentage (Pct_Brk) may be determined by a filtered unity minus theaforementioned ratio, as set forth in Eq. 5 below:

$\begin{matrix}{{Pct\_ Brk} = {1 - \frac{T_{regen}}{T_{friction} + T_{regen}}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

In general, a relatively low braking percentage may indicate thatbraking is mostly done with regenerative braking. Conversely, arelatively high braking percentage may indicate that braking is mostlydone with friction braking.

For conventional vehicles, the braking percentage (Pct_Brk) may bedetermined from one or more of the brake pedal signals (e.g., Brk_SWand/or Brk_Ped_Flg). As understood by one of ordinary skill in the art,the brake switch signal (Brk_SW) may be an input that indicates when thebrake pedal is first being pressed. The brake pedal flag signal(Brk_Ped_Flg) may be a redundant brake pedal input that indicates whenthe brake pedal is being pressed beyond a point signaled by the brakeswitch signal (Brk_SW). In some applications, only one brake pedalsignal may be available and, thus, the signals may be substituted forone another. According to one or more embodiments, the brakingpercentage (Pct_Brk) in conventional vehicles may be a slowly filteredweighted sum of the brake pedal switches. In general, if one of thebrake pedal switches is active, the braking percentage may be relativelylow; if two of the brake pedal switches are active, then the brakingpercentage may be relatively high. The braking percentage (Pct_Brk) mayalso be modified as a function of vehicle speed (V_(spd)) to obtain anormalized braking percentage value (Pct_Brk_(norm)).

The acceleration, deceleration, vehicle speed, total powertrain outputpower, accelerator pedal position change and braking percentage may benormalized with respect to vehicle speed because a vehicle may behavedifferently at lower speeds than it does at higher speeds. Moreover, thesystem may want to account for the vehicle speed when determining thedriving behavior feedback signals 28. For instance, the system may wantto deemphasize a driver's pedal response at low speeds. Accordingly, thecontroller 22 may calculate the normalized accelerator pedal positionchange (ΔAcc_Ped_(norm)) to adjust for vehicle speed. Also, the maximumtotal powertrain output power (P_(max)) may generally be lower at lowerspeeds and the maximum vehicle acceleration (A_(max)) may generally behigher at lower speeds. Normalization of these input values can allowfor the system to take vehicle speed into account when providing drivingbehavior feedback.

The controller 22 may further include a behavior learning and adaptiveinput normalizer block 48 and an instantaneous score determination block50. The normalized outputs of the input process and normalization block46 may become inputs to the behavior learning and adaptive inputnormalizer block 48 and/or the instantaneous score determination block50. At the behavior learning and adaptive input normalizer block 48, thecontroller 22 may monitor a driver's instantaneous driving behavior viaone or more instantaneous driving behavior feedback signals 52 (e.g.,the instantaneous acceleration score (S_(a)), the instantaneousdeceleration score (S_(d)), or the instantaneous cruising speed score(S_(c))) output by the instantaneous score determination block 50. Theinstantaneous driving behavior feedback signals 52 may also betransmitted to the user interface 24. The controller 22 may evaluate thedriver's general acceptance or rejection of short-term driving behaviorfeedback based on the instantaneous driving behavior feedback signals52. In this manner, the controller 22 may learn or adapt to the driver'slong-term driving behavior intentions based upon whether the driver isresponsive to the feedback or generally ignores the feedback.

Moreover, the controller 22 may generate one or more long-term drivingbehavior feedback signals 54 (e.g., the long-term acceleration score(L_(a)), the long-term deceleration score (L_(d)), or the long-termcruising speed score (L_(a))), which may be transmitted to the userinterface 24. Additionally, the long-term driving behavior feedbacksignals 54 may be used to further modify the normalized inputs foracceleration, deceleration and vehicle speed. For example, in one ormore embodiments, the controller 22 may adapt the normalizedacceleration input (A_(norm)) based on whether the driver is responsiveto driving acceleration behavior feedback. In this regard, thenormalized acceleration (A_(norm)) may be multiplied by the long-termacceleration score (L_(a)) at the behavior learning and adaptive inputnormalizer block 48 to generate an adapted normalized acceleration value(A_(adapted)). The controller 22 may also modify the normalized inputsfor deceleration and/or vehicle speed in a similar manner at thebehavior learning and adaptive input normalizer block 48 to generate anadapted normalized deceleration (D_(adapted)) and an adapted normalizedvehicle speed (V_(adapted)), respectively.

In general, the system may convey short-term and/or long-term drivingbehavior feedback during particular driving behavior events. Forinstance, the system may convey driving acceleration behavior feedbackwhen the controller 22 determines that a qualifying acceleration eventis occurring or has just occurred. According to one or more embodiments,the controller 22 may detect the occurrence of an acceleration eventwhen accelerator pedal position is above a pedal position threshold,vehicle speed is above a speed threshold, and vehicle acceleration isabove an acceleration threshold. The system may convey brakingdeceleration behavior feedback when the controller 22 determines that aqualifying deceleration (braking) event is occurring or has justoccurred. According to one or more embodiments, the controller 22 maydetect the occurrence of a deceleration event when the brakingpercentage is above a braking percentage threshold, vehicle speed isabove a speed threshold, and vehicle deceleration is above adeceleration threshold. The system may convey cruising speed behaviorfeedback when the controller 22 determines that a cruising event isoccurring. The controller 22 may detect the occurrence of a cruisingevent when no acceleration or deceleration events are occurring and thevehicle speed is above a minimum speed threshold. According to one ormore embodiments, the controller 22 may convey cruising speed behaviorfeedback when the vehicle acceleration is below an accelerationthreshold and the vehicle deceleration is below a decelerationthreshold. The long-term driving behavior feedback signals 54 may beused to further modify or adapt the normalized inputs for acceleration,deceleration and vehicle speed, as described above, when an accelerationevent, a deceleration event, or a cruising event is detected.

The adapted normalized acceleration (A_(adapted)) can be used incalculating future instantaneous acceleration scores (S_(a)). To thisend, the adapted normalized acceleration (A_(adapted)) may be receivedas an input to the instantaneous score determination block 50.Similarly, the adapted normalized deceleration (D_(adapted)) and adaptednormalized vehicle speed (V_(adapted)) can be used in calculating futureinstantaneous deceleration scores (S_(d)) and instantaneous cruisingspeed scores (S_(c)), respectively. Accordingly, the adapted normalizeddeceleration (D_(adapted)) and adapted normalized vehicle speed(V_(adapted)) may also be received as inputs to the instantaneous scoredetermination block 50. As shown, the instantaneous score determinationblock 50 may also receive additional inputs that may be used tocalculate the instantaneous driving behavior scores. For example, thenormalized total powertrain output power (P_(norm)), the normalizedaccelerator pedal position change (ΔAcc_Ped_(norm)), the normalizedbraking percentage (Pct_Brk_(norm)), and the normalized vehicle speed(V_(norm)) may be inputs to the instantaneous score determination block50.

According to one or more embodiments of the present application, theinstantaneous score determination block 50 may include a fuzzy logiccontroller and/or algorithm for generating one or more of theinstantaneous driving behavior feedback signals 52. As previouslydescribed, the instantaneous driving behavior feedback signals 52 may bereceived at the behavior learning and adaptive input normalizer block 48in order to evaluate the driver's general acceptance or rejection of thedriving behavior feedback and provide long-term driving behaviorfeedback signals 54 to the user interface 24. In one or moreembodiments, the instantaneous driving behavior feedback signals 52 mayalso be transmitted to the user interface 24 for display purposes alongwith the long-term driving behavior feedback signals 54.

FIG. 3 illustrates a simplified, schematic block diagram of thecontroller algorithms generally described in FIG. 2 for use in coachingacceleration behavior. As shown, the controller 22 may generally includethe input process and normalization block 46, the behavior learning andadaptive input normalizer block 48, and the instantaneous scoredetermination block 50. At the input process and normalization block 46,the controller 22 may receive one or more of the input signals 26. Aspreviously described, the one or more input signals 26 may be indicativeof the vehicle acceleration (A_(actual)), total powertrain output power(P_(total)), accelerator pedal position change (ΔAcc_Ped), and vehiclespeed (V_(spd)). Moreover, the vehicle acceleration, total powertrainoutput power, and accelerator pedal position change may each benormalized as a function of the vehicle speed. In this regard, thecontroller 22 may compute the normalized acceleration (A_(norm)) atblock 56 in response to the acceleration (A_(actual)) and vehicle speed(V_(spd)) inputs. In order to compute the normalized acceleration(A_(norm)), the controller 22 may determine a maximum acceleration(A_(max)) value for the vehicle at the current vehicle speed. Themaximum acceleration may be obtained in any number of ways as would beunderstood by one of ordinary skill in the art (e.g., a look-up table,an acceleration curve, etc.). Once the maximum acceleration (A_(max)) isdetermined, the normalized acceleration (A_(norm)) may be computed bydividing the actual acceleration (A_(actual)) by the maximumacceleration (A_(max)):

$\begin{matrix}{A_{norm} = \frac{A_{actual}}{A_{\max}}} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

The controller 22 may compute the normalized total powertrain outputpower (P_(norm)) at block 58 in response to the total powertrain outputpower (P_(total)) and vehicle speed (V_(spd)) inputs. In order tocompute the normalized total powertrain output power (P_(norm)), thecontroller 22 may determine a maximum powertrain output power (P_(max))value for the vehicle at the current vehicle speed. The maximumpowertrain output power may be obtained in any number of ways as wouldbe understood by one of ordinary skill in the art (e.g., a look-uptable, a power curve, etc.). Once the maximum powertrain output power(P_(max)) is determined, the normalized total powertrain output power(P_(norm)) may be computed by dividing the total powertrain output power(P_(total)) by the maximum powertrain output power (P_(max)):

$\begin{matrix}{P_{norm} = \frac{P_{total}}{P_{\max}}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

The controller 22 may compute the normalized accelerator pedal positionchange (ΔAcc_Ped_(norm)) at block 60 in response to the acceleratorpedal position change (ΔAcc_Ped) and vehicle speed (V_(spd)) inputs. Inorder to compute the normalized accelerator pedal position change(ΔAcc_Ped_(norm)), the controller 22 may determine a maximum acceleratorpedal position change (ΔAcc_Ped_(max)) value recognized by the controlsystem 20 at the current vehicle speed. The maximum accelerator pedalposition change may be obtained in any number of ways as would beunderstood by one of ordinary skill in the art (e.g., a look-up table,an accelerator pedal response curve, etc.). Once the maximum acceleratorpedal position change (ΔAcc_Ped_(max)) is determined, the normalizedaccelerator pedal position change (ΔAcc_Ped_(norm)) may be computed bydividing the accelerator pedal position change (ΔAcc_Ped) by the maximumaccelerator pedal position change (ΔAcc_Ped_(max)):

$\begin{matrix}{{\Delta \; {Acc\_ Ped}_{norm}} = \frac{\Delta \; {Acc\_ Ped}}{\Delta \; {Acc\_ Ped}_{\max}}} & {{Eq}.\mspace{14mu} 8}\end{matrix}$

As previously described, the driving acceleration behavior feedback maygenerally be provided during vehicle acceleration events. Accordingly,the long-term acceleration behavior feedback signal may be used tofurther modify the normalized input for acceleration (A_(norm)) when theaccelerator pedal position is above a threshold, vehicle speed is abovea threshold, and vehicle acceleration is above a threshold. To this end,the normalized acceleration (A_(norm)) generated at block 56 may bemultiplied by the long-term acceleration score (L_(a)) at multiplicationjunction 62 to produce the adapted normalized acceleration(A_(adapted)). The algorithm for generating the long-term accelerationscore (L_(a)) is described in greater detail below. The controller 22may determine the instantaneous acceleration score (S_(a)) at block 64.The adapted normalized acceleration (A_(adapted)), output frommultiplication junction 62, may be an input to the instantaneousacceleration score determination block 64. The normalized totalpowertrain output power (P_(norm)) and the normalized accelerator pedalposition change (ΔAcc_Ped_(norm)) may also be inputs to theinstantaneous acceleration score determination block 64.

According to one or more embodiments of the present application, theinstantaneous acceleration score (S_(a)) may be transmitted to the userinterface 24 and displayed via the display 30. Additionally, theinstantaneous acceleration score (S_(a)) may be compared to a functionof the long-term acceleration score (f(L_(a))) at block 66. Since thelong-term acceleration score (L_(a)) may be based on the instantaneousacceleration score (S_(a)), the controller 22 can determine whether thedriver's instantaneous acceleration behavior will generally increase ordecrease the long-term acceleration score (L_(a)). Further, thecontroller 22 may select a forgetting factor (w) based on the comparisonbetween the instantaneous acceleration score (S_(a)) and the function ofthe long-term acceleration score (L_(a)). For instance, if theinstantaneous acceleration score (S_(a)) is greater than the long-termacceleration score (L_(a)), then it may be determined that the long-termacceleration score (L_(a)) will be increasing. If the long-termacceleration score (L_(a)) will be increasing, the controller 22 mayoutput an increasing forgetting factor (w_(i)) at comparison block 66.On the other hand, if the instantaneous acceleration score (S_(a)) isless than the long-term acceleration score (L_(a)), then it may bedetermined that the long-term acceleration score (L_(a)) will bedecreasing. In this case, the controller 22 may output a decreasingforgetting factor (w_(d)) at comparison block 66. Once the appropriateforgetting factor (w) is determined, the controller 22 may calculate anew long-term acceleration score (L_(a)) at block 68 based upon theprevious long-term acceleration score, the instantaneous accelerationscore, and the applicable forgetting factor. According to one or moreembodiments of the present application, the new long-term accelerationscore may be calculated according to Eq. 9 shown below:

L _(a(n)) =L _(a(n−1))(w)+S _(a)(1−w)   Eq. 9

Where:

L_(a(n))=the new long-term acceleration score

L _(a(n−1))=the previous long-term acceleration score

S_(a)=the instantaneous acceleration score

w=the forgetting factor (e.g., w_(i) or w_(d))

The term “long-term” in the long-term acceleration score (L_(a)) may bea relative one. With respect to the instantaneous acceleration score(S_(a)), the long-term acceleration score (L_(a)) may provide driverswith relatively long-term feedback on their driving behavior. In thisregard, the long-term acceleration score (L_(a)) may reflect overalldriving acceleration behavior over a moving period of several seconds toseveral minutes or even hours. The value of the forgetting factor (w)may be chosen to reflect the length of the moving period. The higher theforgetting factor, the greater the weight that may be placed on thelong-term acceleration score (L_(a)). According to one or moreembodiments, the increasing forgetting factor (w_(i)) may be set greaterthan the decreasing forgetting factor (w_(d)) so that the instantaneousacceleration score (S_(a)) may have less impact on the long-termacceleration score (L_(a)) when the long-term acceleration score isincreasing (i.e., L_(a)<S_(a)).

An increasing long-term acceleration score (L_(a)) may be an indicationthat the driver is accepting or otherwise responding to the drivingacceleration behavior feedback. A decreasing long-term accelerationscore (L_(a)) may provide an indication that the driver is generallyrejecting or otherwise ignoring the driving acceleration behaviorfeedback. If the driver generally ignores the acceleration behaviorfeedback, such that over time the driver may have a relatively lowlong-term acceleration score (L_(a)), then the system may adapt thedriving acceleration behavior feedback it provides so as to be lesscritical of inefficient acceleration behavior. Stated differently, thefeedback conveyed by the system for relatively poor driving accelerationbehavior events may not be as penal or otherwise adversely affect thelong-term acceleration score (L_(a)) for routinely aggressive drivers,that tend not to heed the acceleration behavior coaching, as compared todrivers with traditionally good driving acceleration behavior. Thus, ifthe driver is generally receptive to the driving acceleration behaviorfeedback by modifying his or her acceleration behavior accordingly, thenthe system may be more sensitive with respect to future accelerationbehavior events in order to continue encouraging further behaviormodification. To this end, the controller 22 may use the long-termacceleration score (L_(a)) to adapt the normalized acceleration input tothe instantaneous acceleration score determination block 64 so that thedriving acceleration behavior feedback is more critical of, orresponsive to, relatively nonaggressive drivers. As previouslydescribed, the normalized acceleration input (A_(norm)) may bemultiplied by the long-term acceleration score (L_(a)) at multiplicationjunction 62 to generate the adapted normalized acceleration input(A_(adapted)).

FIG. 4 is functional block diagram illustrating a fuzzy logic controlalgorithm 70 in accordance with one or more embodiments of the presentapplication. The fuzzy logic control algorithm 70 may correspond todetermination block 64 in FIG. 3 for determining the instantaneousacceleration score (S_(a)). The fuzzy logic control algorithm 70 may becarried out using a fuzzy logic controller. The fuzzy logic controllermay be contained within the controller 22, and may be implemented inhardware and/or software control logic as described in greater detailherein. As shown, the adapted normalized acceleration (A_(adapted)), thenormalized total powertrain output power (P_(norm)), and the normalizedaccelerator pedal position change (ΔAcc_Ped_(norm)) may be fuzzy inputvariables (x_(i)).

Input membership functions may be applied to the normalized fuzzy inputs(x_(i)) at fuzzification block 72. With general reference to FIGS. 5a-c, exemplary input membership functions (Ã_(i,j)) for the fuzzy inputvariables (x_(i)) are illustrated in accordance with one or moreembodiments of the present application. As shown, each fuzzy input(x_(i)) may have a corresponding input membership function (Ã_(i,j))having two truth values—one for LOW and one for HIGH. The truth valuesmay be referred to herein as input membership values, μ_(i)(x_(i),A_(i,j)). Thus, each input membership function (Ã_(i,j)) may be used togenerate the input membership values, μ_(i)(x_(i),Ã_(i,j)), for fuzzyrule antecedents of “LOW” and “HIGH” for a given normalized fuzzy input(x_(i)). With specific reference to FIG. 5 a, an input membershipfunction (Ã_(1,j)) for use in generating input membership values,μ_(i)(x₁, Ã_(1,j)) for the normalized accelerator pedal position changefuzzy input (x₁=ΔAcc_Ped_(norm)) is illustrated. As an example, verticalline 74 may represent a particular normalized accelerator pedal positionchange input value (x₁). As shown, the input membership value for theantecedent of “LOW” may be 0.8, while the input membership value for theantecedent of “HIGH” may be 0.2.

FIG. 5 b illustrates an input membership function (Ã_(2,j)) for use ingenerating input membership values, μ_(i)(x₂,Ã_(2,j)), for the adaptednormalized acceleration fuzzy input (x₂=A_(adapted)). Vertical line 76may represent a particular adapted normalized acceleration input value(x₂). As shown in this example, the input membership value for theantecedent of “LOW” may be 0.9, while the input membership value for theantecedent of “HIGH” may be 0.1. FIG. 5 c illustrates an inputmembership function (Ã_(3,j)) for use in generating the input membershipvalues, μ_(j)(x₃,Ã_(3,j)), for the normalized total powertrain outputpower fuzzy input (x₃=P_(nor)). Vertical line 78 may represent aparticular normalized total powertrain output power input value (x₃). Asshown in the example, the input membership value for the antecedent of“LOW” may be 0.4, while the input membership value for the antecedent of“HIGH” may be 0.6.

Referring back to FIG. 4, the fuzzy logic controller may apply a set offuzzy rules at block 80 for use in generating a plurality of outputmembership values ({tilde over (h)}_(k,j)). FIG. 6 shows a table 82illustrating an exemplary set of fuzzy rules in accordance with one ormore embodiments of the present application. In the illustratedembodiment, eight (8) fuzzy rules are shown corresponding to the numberof event multiples for the three normalized fuzzy inputs for acceleratorpedal position change (x₁), adapted normalized acceleration (x₂), andnormalized total powertrain output power (x₃), each of which has twopossible outcomes (e.g., HIGH or LOW). Each row in table 82 below aheader 84 may correspond to a different fuzzy rule, the j-th rule. Table82 may include three antecedent columns 86 after a rule number column88. The antecedent columns 86 generally depict the rule antecedents forthe three fuzzy input variables (x₁): normalized accelerator pedalposition change (x₁), adapted normalized acceleration (x₂), andnormalized total powertrain output power (x₃). As described above, therule antecedents may relate to the input membership values,μ_(j)(x_(i),Ã_(i,j)).

Each fuzzy rule in the illustrated embodiment may also have three ruleconsequents. Accordingly, table 82 may further include three consequentcolumns 90 adjacent to the antecedent columns 86. The three ruleconsequents may correspond to three fuzzy output variables, referred toas defuzzified outputs (y_(k)), which can be used to determine theinstantaneous acceleration score (S_(a)). According to one or moreembodiments, the three fuzzy output variables may include an advisedchange in acceleration score (y₁), a maximum acceleration score offset(y₂), and a minimum acceleration score offset (y₃). The advised changein acceleration score output (y₁) may correspond to a recommended changein vehicle acceleration requested by the driver via the acceleratorpedal. The maximum acceleration score offset output (y₂) may correspondto a maximum advised change in the driver requested power. The minimumacceleration score offset output (y₃) may correspond to a minimumadvised change in the driver requested acceleration. Since the ultimateoutput to the fuzzy logic algorithm 70 is the instantaneous accelerationscore (S_(a)), the advised change in driver acceleration may generallycorrespond to a change in the instantaneous acceleration score, as willbe described in greater detail below.

The rule consequents may be used in the generation of the outputmembership values ({tilde over (h)}_(k,j)). For instance, each fuzzyoutput variable (y_(k)) may be associated with an output membershipfunction for determining the output membership values ({tilde over(h)}_(k,j)) used to calculate the defuzzified output values. FIG. 7depicts a simplified, exemplary output membership function 92 fordetermining the output membership value ({tilde over (h)}_(1,j)) for agiven rule consequent of the advised change in acceleration score fuzzyoutput variable (y₁). As seen therein, the output membership value forthe consequent of “HIGH” may be 0.5, whereas the output membership valuefor the consequent of “LOW” may be 0.1. The output membership value forthe consequent of “−HIGH” may be −0.5, whereas the output membershipvalue for the consequent of “−LOW” may be −0.1. Although FIG. 7 depictsan exemplary output membership function for determining outputmembership values ({tilde over (h)}_(k,j)) for the advised change inacceleration score rule consequents (where k=1), similar outputmembership functions may be applied for determining the outputmembership values associated with the maximum acceleration score offsetand minimum acceleration score offset rule consequents. Alternatively,the maximum and minimum acceleration score offsets may be optional fuzzylogic output variables. In this manner, all the rule consequents for themaximum acceleration score offset output (y₂) may effectively be HIGH(where HIGH=1). Similarly, all the rule consequents for the minimumacceleration score offset output (y₃) may effectively be LOW (whereLOW=0). Moreover, different output membership values ({tilde over(h)}_(1,j)) than those that are shown may be provided by outputmembership function 92 for the rule consequents for the advised changein acceleration score fuzzy output variable, depending upon theparticular implementation.

Referring back to FIG. 6, table 82 may further include a comments column94, which provides a brief description of the driving accelerationbehavior conditions satisfying each fuzzy rule and, in some instances, aproposed system response for providing feedback to the driver when theconditions are met. For instance, with reference to the first fuzzy rule(j=1), the system may provide positive instantaneous accelerationbehavior feedback when the driver pedal response is steady or in the lowrange, the vehicle acceleration is in the low range, and the total poweris in the low range. With reference to the second fuzzy rule (j=2), thesystem may provide relatively slow positive instantaneous accelerationbehavior feedback when the driver pedal response is steady or in the lowrange, the vehicle acceleration is in the low range, and total power isin the higher range. The preceding scenario can occur under colderclimate conditions when the powertrain may be cold and requires morepower for heating powertrain components. This condition may also coverthe case when a vehicle is going uphill and, therefore, is consumingmore power.

With reference to the third fuzzy rule (j=3), the system may providerelatively slow positive instantaneous acceleration behavior feedbackwhen the driver pedal response is steady or in the low range, thevehicle acceleration is in the higher range, and the total power is inthe low range. This scenario can occur under downhill conditions whenacceleration may be higher while the total power is in the lower range.With reference to the fourth fuzzy rule (j=4), the system may providenegative instantaneous acceleration behavior feedback when the driverpedal response is steady or in low range, the vehicle acceleration is inthe higher range, and the total power is in the higher range. This casecan occur when the vehicle is engaging in a clearly inefficientoperation, such as during relatively aggressive driving.

With reference to the fifth fuzzy rule (j=5), the system may prepare foran anticipated negative instantaneous acceleration behavior feedbackwhen the driver pedal response is transient or in the high range, thevehicle acceleration is in the low range, and the total power is in thelow range. This scenario can occur when the current conditions arerelatively efficient, but based on the driver pedal response, the systemmay anticipate or otherwise predict that an inefficient operation isforthcoming in the near future. With reference to the sixth fuzzy rule(j=6), the system may prepare for an anticipated negative instantaneousacceleration behavior feedback when the driver pedal response istransient or in the high range, the vehicle acceleration is in the lowrange, and the total power is in the high range. This scenario can occurwhen a vehicle is going uphill and, as a result, the vehicleacceleration may already be low but the total power may be high.Therefore, any transient increase in driver pedal response may notnecessarily reflect an energy inefficient operation, but the system maynevertheless prepare for possible inefficiency if such a condition wasto disappear. This scenario may also cover the case when the powertrainis cold and requires more power for heating the powertrain components.

With reference to the seventh fuzzy rule (j=7), the system may prepareto provide a slow negative instantaneous acceleration behavior feedbackwhen the driver pedal response is transient or in the high range, thevehicle acceleration is in the high range, and the total power is in thelow range. This scenario can occur when a vehicle is going downhill and,as a result, the vehicle acceleration may already be high but the totalpower may be low. Therefore, any transient increase in driver pedalresponse could result in future energy inefficiency. Accordingly, thesystem may anticipate this potential future energy inefficiency bypreparing to provide the slow negative instantaneous accelerationbehavior feedback.

With reference to the eighth fuzzy rule (j=8), the system may provide anegative instantaneous acceleration behavior feedback when the driverpedal response is transient or in the high range, the vehicleacceleration is in the high range, and the total power is in the highrange.

According to one or more embodiments of the present application, thej-th rule operation may be represented using the following expression:

μ_(j)(x₁,Ã_(i,j))μ_(j)(x₂,Ã_(2,j))μ_(j)(x₃,Ã_(3,j))

Where:

x_(i)=normalized fuzzy input variables (i=1,2,3)

Ã_(i,j)=input membership functions

μ_(j)(x_(i),Ã_(i,j))=input membership value of the rule antecedent ofthe j-th rule for a given normalized input (x_(i)) and its correspondinginput membership function (Ã_(i,j))

Referring back to FIG. 4, once determined, the output membership valuesets ({tilde over (h)}_(k,j)) and the input membership value sets(μ_(j)(_(i),Ã_(i,j))) may undergo defuzzification at block 96. Atdefuzzification block 96, fuzzy operator implication and aggregation mayoccur using the input and output membership value sets. The controller22 may calculate the outputs of defuzzification (y_(k)) according to Eq.10 as set forth below:

$\begin{matrix}{{{y_{k} = \frac{\sum\limits_{j = 1}^{\Omega}{{\mu_{j}\left( {x_{1},{\overset{\sim}{A}}_{1,j}} \right)}{\mu_{j}\left( {x_{2},{\overset{\sim}{A}}_{2,j}} \right)}{\mu_{j}\left( {x_{3},{\overset{\sim}{A}}_{3,j}} \right)}{\overset{\sim}{h}}_{k,j}}}{\sum\limits_{j = 1}^{\Omega}{{\mu_{j}\left( {x_{1},{\overset{\sim}{A}}_{1,j}} \right)}{\mu_{j}\left( {x_{2},{\overset{\sim}{A}}_{2,j}} \right)}{\mu_{j}\left( {x_{3},{\overset{\sim}{A}}_{3,j}} \right)}}}};}{{k = 1},2,3}} & {{Eq}.\mspace{14mu} 10}\end{matrix}$

Where:

Ω=total number of fuzzy rules (e.g., eight)

x_(i)=normalized fuzzy input variables (i=1,2,3)

Ã_(i,j)=input membership functions

μ_(j)(x_(i),Ã_(i,j))=input membership value of the rule antecedent ofthe j-th rule for a given normalized input (x_(i)) and its correspondinginput membership function (Ã_(i,j))

{tilde over (h)}_(k,j)=output membership value of the rule consequent ofthe j-th rule for a given fuzzy output variable and its correspondingoutput membership function

As set forth above, the controller 22 may generate three (3) defuzzifiedoutputs (y_(k)) at defuzzification block 96. Moreover, the threedefuzzified outputs (y_(k)) may be associated with the three ruleconsequents shown by table 82 in FIG. 6, namely: the advised change inacceleration score, the maximum acceleration score offset, and theminimum acceleration score offset.

FIG. 8 is an exemplary flow diagram illustrating an implementation ofthe defuzzification algorithm using a real-world example. Table 98demonstrates how a denominator 100 from Eq. 10 may be calculated. Asshown in table 98, the antecedents of “LOW” and “HIGH” for each fuzzyrule have been replaced by the corresponding input membership values,μ_(i)(x_(i),Ã_(i,j)), for each normalized input (x_(i)) and its inputmembership function (Ã_(i,j)). In this example, the input membershipvalues in table 98 correspond to the input membership values obtainedusing the input membership functions shown in FIGS. 5 a-c for theparticular normalized inputs (x_(i)) depicted by vertical lines 74, 76,78. These input membership values may become the antecedent operatorsfor the j-th rule operation,μ_(i)(x₁,Ã_(1,j))μ_(i)(x₂,Ã_(2,j))μ_(i)(x₃,Ã_(3,j)), as shown in column102. The results of the j-th rule operations are shown in column 104.The results of the j-th rule operations may be aggregated. The sum ofthe j-th rule operations is shown at the bottom of column 104 in cell106. The sum of the results of the j-th rule operations becomes thedenominator 100 for calculating the defuzzified outputs (y_(k)) as setforth in Eq. 10. As shown, the summation for the j-th rule operations isequal to one (1).

Table 108 demonstrates how a numerator 110 from Eq. 10 may becalculated. As shown in table 108, the consequents of “LOW,” “HIGH,”“−LOW” and “−HIGH” may be replaced by the corresponding outputmembership values ({tilde over (h)}_(k,j)) for each fuzzy outputvariable (where k=1,2,3) and its corresponding output membershipfunction. For explanation purposes, in this example, table 108 onlyshows the output membership value set ({tilde over (h)}_(1,j)) for usein calculating the numerator 110 of the defuzzified output (y₁), whichcorresponds to the advised change in acceleration score output variable(where k=1). However, the numerator 110 for each defuzzified output(y_(k)) may be calculated in a similar fashion using the associatedoutput membership value sets ({tilde over (h)}_(k,j)). In this example,the output membership values ({tilde over (h)}_(1,j)) may correspond tothe exemplary output membership values obtained from the sample outputmembership function 92 shown in FIG. 7. By way of fuzzy implication,each result from the j-th rule operation,μ_(j)(x₁,Ã_(1,j))μ_(j)(x₂,Ã_(2,j))μ_(j)(x₃,Ã_(3,j)), may be multipliedby its corresponding output membership value ({tilde over (h)}_(1,j)),as shown in column 112. The results of the fuzzy implications are shownin column 114. The results of the fuzzy implications may also beaggregated. The sum of the aggregated results is shown at the bottom ofcolumn 114 in cell 116. The sum of the results of the fuzzy implicationsbecomes the numerator 110 for the defuzzified outputs (y_(k)) as setforth in Eq. 10. As shown in FIG. 8, in this example, the numerator 110for the deffuzified output (y₁) is equal to 0.1656.

Referring back to FIG. 4, once all the defuzzified outputs (y_(k)) aregenerated at defuzzification block 96, the controller 22 may determinethe fuzzy logic output as provided at block 118. As shown, the fuzzylogic output may be the instantaneous acceleration score (S_(a)). Thecontroller 22 may calculate the instantaneous acceleration score (S_(a))from the defuzzified outputs (y_(k)) according to Eq. 11 and Eq. 12 asset forth below:

$\begin{matrix}{S_{tmp} = {\int{y_{1}{t}}}} & {{Eq}.\mspace{14mu} 11} \\{S_{a} = \left\{ \begin{matrix}{{1 + y_{2}},} & {{{if}\mspace{14mu} S_{tmp}} > \left( {1 + y_{2}} \right)} \\{S_{tmp},} & {{{if}\mspace{14mu} y_{3}} \leq S_{tmp} \leq \left( {1 + y_{2}} \right)} \\{y_{3},} & {{{if}\mspace{14mu} S_{tmp}} < y_{3}}\end{matrix} \right.} & {{Eq}.\mspace{14mu} 12}\end{matrix}$

Where:

y_(k)=defuzzified outputs (k=1,2,3)

y₁: advised change in acceleration score

y₂: max acceleration score offset

y₃: min acceleration score offset

Once determined, the instantaneous acceleration score (S_(a)) may betransmitted to the user interface 24 and conveyed to a driver usingdisplay 30. The instantaneous acceleration score (S_(a)) may be conveyedto the driver using the acceleration feedback gauge 32 a. According toone or more embodiments, the location of the acceleration feedbackindicator 34 along the acceleration feedback gauge 32 a may correspondto the instantaneous acceleration score (S_(a)). Additionally oralternatively, the color of at least a portion of the accelerationfeedback gauge 32 a may be associated with the instantaneousacceleration score (S_(a)). For instance, when the instantaneousacceleration score (S_(a)) is within a first range, at least a portionof the acceleration feedback gauge 32 a may be displayed in a firstcolor. Further, when the instantaneous acceleration score (S_(a)) iswithin a second range, at least a portion of the acceleration feedbackgauge 32 a may be displayed in a second color different from the first.Moreover, when the instantaneous acceleration score (S_(a)) is within athird range, at least a portion of the acceleration feedback gauge 32 amay be displayed in a third color, which may be different from the firstand second color. Fewer or greater instantaneous acceleration scoreranges and associated colors may be implemented to convey theinstantaneous acceleration score (S_(a)) in accordance with one or moreembodiments of the present application.

Additionally, as previously described, the instantaneous accelerationscore (S_(a)) may be used to calculate the long-term acceleration score(L_(a)) as set forth above in Eq. 9. The long-term acceleration score(L_(a)) may be transmitted to the user interface 24 and conveyed to adriver using display 30. The long-term acceleration score (L_(a)) may beconveyed to the driver using the acceleration feedback gauge 32 a.According to one or more embodiments, the location of the accelerationfeedback indicator 34 along the acceleration feedback gauge 32 a maycorrespond to the long-term acceleration score (L_(a)). In this case,the instantaneous acceleration score (S_(a)) may be conveyed by the userinterface 24 in another manner (e.g., the color of at least a portion ofthe acceleration feedback gauge 32 a), or not at all. Additionally oralternatively, the color of at least a portion of the accelerationfeedback gauge 32 a may also be associated with the long-termacceleration score (L_(a)).

FIG. 9 is a simplified, exemplary flow chart 900 depicting a method forconveying driving acceleration behavior feedback in accordance with oneor more embodiments of the present application. At step 905, the systemmay receive inputs such as input signals 26. The input signals 26 may begenerally indicative of vehicle speed (V_(spd)), vehicle acceleration(A_(actual)), total powertrain output power (P_(total)), and/or theaccelerator pedal position change (ΔAcc_Ped). Exemplary input signalsmay include an accelerator pedal position signal (APPS), high-voltage(HV) battery power (P_(batt)), fuel flow rate (Fuel_Flow), vehicle speed(V_(spd)) and/or output shaft speed (ω_(oss)). (The system may computevehicle acceleration (A_(actual)), total powertrain output power(P_(total)), and/or the accelerator pedal position change (ΔAcc_Ped)from the input signals 26 at step 910. The vehicle acceleration(A_(actual)) may be calculated from the vehicle speed (V_(spd)) and/oroutput shaft speed (ω_(oss)). The total powertrain output power(P_(total)), may be calculated differently depending upon the powertrainconfiguration. In general, the total powertrain output power (P_(total))may be the sum of the battery power (P_(batt)) and fuel power(P_(fuel)). However, depending upon the powertrain technology, eitherthe battery power (P_(batt)) or fuel power (P_(fuel)) may be equal tozero. The accelerator pedal position change (ΔAcc_Ped) may be determinedfrom the accelerator pedal position signal (APPS).

At step 915, the vehicle acceleration (A_(actual)), total powertrainoutput power (P_(total)), and accelerator pedal position change(ΔAcc_Ped) may be normalized. In particular, the vehicle acceleration(A_(actual)), total powertrain output power (P_(total)), and acceleratorpedal position change (ΔAcc_Ped) may be modified as a function ofvehicle speed (V_(spd)) to obtain the normalized acceleration(A_(norm)), the normalized total powertrain output power (P_(norm)) andthe normalized (P_(norm)), accelerator pedal position change(ΔAcc_Ped_(norm)), respectively. The acceleration, total powertrainoutput power, and accelerator pedal position change may be normalizedwith respect to vehicle speed to adjust for vehicle behavior andoperating characteristics at different speeds, as well as account forthe vehicle speed when determining the driving acceleration behaviorfeedback.

At step 920, system may determine whether an acceleration event hasoccurred or is occurring. The system may convey driving accelerationbehavior feedback when an acceleration event is detected. According toone or more embodiments, an acceleration event may be detected when theaccelerator pedal position is above a pedal position threshold, thevehicle speed is above a speed threshold, and the vehicle accelerationis above an acceleration threshold. If no acceleration event isdetected, the method may return to step 905 where the input signals 26can continue to be monitored. If, on the other hand, an accelerationevent is detected at step 920, the method may proceed to step 925.

At step 925, the system may calculate the adapted normalizedacceleration (A_(adapted)). According to one or more embodiments, thenormalized acceleration input (A_(norm)) may be modified based on driverresponsiveness to the driving acceleration behavior feedback. In thisregard, the normalized acceleration (A_(norm)) may be multiplied by thelong-term acceleration score (L_(a)) to generate the adapted normalizedacceleration (A_(adapted)). At step 930, the system may calculate theinstantaneous acceleration score (S_(a)) based upon the adaptednormalized acceleration (A_(adapted)), the normalized total powertrainoutput power (P_(norm)), and the normalized accelerator pedal positionchange (ΔAcc_Ped_(norm)). In one or more embodiments, the instantaneousacceleration score (S_(a)) may be output to the user interface 24 whereit may be conveyed to a driver, as provided at step 935. Theinstantaneous acceleration score (S_(a)) may be conveyed to the driverusing the acceleration feedback gauge 32 a. According to one or moreembodiments, the location of the acceleration feedback indicator 34along the acceleration feedback gauge 32 a may correspond to theinstantaneous acceleration score (S_(a)). Additionally or alternatively,the color of at least a portion of the acceleration feedback gauge 32 amay be associated with the instantaneous acceleration score (S_(a)).

Additionally, the instantaneous acceleration score (S_(a)) may becompared to a function of the long-term acceleration score (f(L_(a))) todetermine whether the driver's instantaneous acceleration behavior willincrease or decrease the long-term acceleration score (L_(a)), at step940. According to one or more embodiments, f(L_(a)) may be set equal toL_(a). In this manner, if the instantaneous acceleration score (S_(a))is greater than the long-term acceleration score (L_(a)), the system mayconclude that the long-term acceleration score is increasing.Accordingly, the system may select an increasing forgetting factor(w_(i)) at step 945. If, on the other hand, the instantaneousacceleration score (S_(a)) is less than the long-term acceleration score(L_(a)), the system may conclude that the long-term acceleration scoreis decreasing. Accordingly, the system may select a decreasingforgetting factor (w_(d)) at step 950. The instantaneous accelerationscore (S_(a)) may be compared to alternative functions of the long-termacceleration score (f(L_(a))) to determine whether the driver'sinstantaneous acceleration behavior will increase or decrease thelong-term acceleration score (L_(a)). Once the appropriate forgettingfactor (w) is selected, the method may proceed to step 955.

At step 955, the system may compute a new long-term acceleration score(L_(a)). According to one or more embodiments of the presentapplication, the new long-term acceleration score (L_(a)) may be basedupon the previous long-term acceleration score, the instantaneousacceleration score (S_(a)), and the selected forgetting factor (w)according to Eq. 9 set forth above. Once calculated, the long-termacceleration score (L_(a)) may be output to the user interface 24 whereit may be conveyed to a driver, as provided at step 960. The long-termacceleration score (L_(a)) may be conveyed to the driver using theacceleration feedback gauge 32 a. According to one or more embodiments,the location of the acceleration feedback indicator 34 along theacceleration feedback gauge 32 a may correspond to the long-termacceleration score (L_(a)). In this case, the instantaneous accelerationscore (S_(a)) may be conveyed by the user interface 24 in another manner(e.g., the color of at least a portion of the acceleration feedbackgauge 32 a), or not at all. Additionally or alternatively, the color ofat least a portion of the acceleration feedback gauge 32 a may also beassociated with the long-term acceleration score (L_(a)).

FIG. 10 is a simplified, exemplary flowchart 1000 depicting the processfor calculating the instantaneous acceleration score (S_(a)) at step 930in FIG. 9 in greater detail in accordance with one or more embodimentsof the present application. As previously described, the instantaneousacceleration score (S_(a)) may be calculated using a fuzzy logicalgorithm. To this end, input membership functions (Ã_(i,j)) may beapplied to obtain input membership values, μ_(j)(x_(i),Ã_(i,j)), for agiven set of normalized fuzzy input variables (x_(i)), at step 1010. Atstep 1020, the set of fuzzy logic rules may be applied to obtain therule consequents for use in generating the set of output membershipvalues ({tilde over (h)}_(k,j)). At step 1030, an output membershipfunction may be applied to the fuzzy rule consequents for each fuzzyoutput variable to obtain the output membership values ({tilde over(h)}_(k,j)). At step 1040, the system may calculate the defuzzifiedoutputs (y_(k)) for each fuzzy output variable according to Eq. 10 setforth above. The first defuzzified output (y₁) may correspond to anadvised change in driver requested acceleration. The second defuzzifiedoutput (y₂) may correspond to a maximum acceleration score offset. Thethird defuzzified output (y₃) may correspond to a minimum accelerationscore offset. At step 1050, the system may calculate the instantaneousacceleration score (S_(a)) based upon the defuzzified outputs (y_(k))according to Eq. 11 and Eq. 12 set forth above. Once the instantaneousacceleration score (S_(a)) is calculated, the method may return to step940 in FIG. 9 for the calculation of the long-term acceleration score(L_(a)).

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms of the invention. Rather,the words used in the specification are words of description rather thanlimitation, and it is understood that various changes may be madewithout departing from the spirit and scope of the invention.Additionally, the features of various implementing embodiments may becombined to form further embodiments of the invention.

What is claimed is:
 1. A control system comprising: a controllerconfigured to receive input indicative of at least vehicle accelerationand powertrain output power, and output at least one acceleration scorebased upon the input; and an interface communicating with the controllerand configured to display an acceleration feedback indicator indicativeof the at least one acceleration score.
 2. The control system of claim1, wherein the interface includes an acceleration feedback gauge fordisplaying the acceleration feedback indicator, and wherein theinterface is configured to adjust the acceleration feedback indicatorwithin the acceleration feedback gauge based on the at least oneacceleration score.
 3. The control system of claim 2, wherein the atleast one acceleration score indicated by the acceleration feedbackindicator includes one of a long-term acceleration score and aninstantaneous acceleration score.
 4. The control system of claim 3,wherein the interface is further configured to adjust a color of atleast a portion of the acceleration feedback gauge based on the other ofthe long-term acceleration score and the instantaneous accelerationscore.
 5. The control system of claim 3, wherein the input is furtherindicative of an accelerator pedal position change.
 6. The controlsystem of claim 5, wherein the controller is further configured tocalculate the instantaneous acceleration score based upon the vehicleacceleration, the powertrain output power and the accelerator pedalposition change.
 7. The control system of claim 6, wherein thecontroller is further configured to normalize one or more of the vehicleacceleration, the powertrain output power and the accelerator pedalposition change based upon vehicle speed prior to calculating theinstantaneous acceleration score.
 8. The control system of claim 6,wherein the controller is further configured to calculate an adaptedacceleration value prior to calculating the instantaneous accelerationscore, the adapted acceleration value being based on the vehicleacceleration and the long-term acceleration score.
 9. The control systemof claim 8, wherein the adapted acceleration value is calculated bymultiplying a normalized acceleration value by the long-termacceleration score.
 10. The control system of claim 8, wherein theinstantaneous acceleration score is calculated using a fuzzy logicalgorithm.
 11. The control system of claim 3, wherein the long-termacceleration score is based at least in part upon the instantaneousacceleration score, a previous long-term acceleration score, and aforgetting factor for weighting the instantaneous acceleration score andthe previous long-term acceleration score.
 12. The control system ofclaim 11, wherein the forgetting factor is based on a comparison of theinstantaneous acceleration score to a function of the long-termacceleration score.
 13. A method for controlling a vehicle displaycomprising: receiving input indicative at least of vehicle accelerationand powertrain output power; calculating at least one acceleration scorebased upon the input; and displaying an acceleration feedback gaugehaving an acceleration feedback indicator indicative of the at least oneacceleration score.
 14. The method of claim 13, wherein the step ofcalculating the at least one acceleration score comprises: calculatingan instantaneous acceleration score based upon the input; andcalculating a long-term acceleration score based on the instantaneousacceleration score.
 15. The method of claim 14, wherein the at least oneacceleration score indicated by the acceleration feedback indicatorincludes one of the long-term acceleration score and the instantaneousacceleration score.
 16. The method of claim 14, wherein the input isfurther indicative of an accelerator pedal position change.
 17. Themethod of claim 16, further comprising: normalizing one or more of thevehicle acceleration, the powertrain output power and the acceleratorpedal position change based upon vehicle speed prior to calculating theinstantaneous acceleration score.
 18. The method of claim 16, furthercomprising: calculating an adapted acceleration value prior tocalculating the instantaneous acceleration score, the adaptedacceleration value being based on the vehicle acceleration and thelong-term acceleration score.
 19. A display control system comprising: acontroller configured to receive input indicative of vehicleacceleration, powertrain output power and accelerator pedal positionchange, calculate an instantaneous acceleration score based on theinput, and provide an acceleration feedback signal corresponding to along-term acceleration score based upon the instantaneous accelerationscore; and a display in communication with the controller and includingan acceleration feedback gauge configured to display an accelerationfeedback indicator indicative of the long-term acceleration score. 20.The display system of claim 19, wherein the display is furtherconfigured to adjust a color of at least a portion of the accelerationfeedback gauge based on the long-term acceleration score.